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Mathematical Modeling in Social Network Analysis: Using TOPSIS to Find Node Influences in a Social Network 被引量:4

Mathematical Modeling in Social Network Analysis: Using TOPSIS to Find Node Influences in a Social Network
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摘要 In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes? Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers' inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature: the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers' inputs or the topology of the network.
出处 《Journal of Mathematics and System Science》 2013年第10期531-541,共11页 数学和系统科学(英文版)
关键词 Social network analysis multi-attribute decision making Analytical hierarchy process (AHP) weighted criterion TOPSIS node influence TOPSIS法 社会网络 网络分析 节点 数学建模 输入功能 灵敏度分析 决策信息
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